Voxel Collapse to BEV.
The voxel features \(\mathbf {V} \in \mathbb {R}^{X \times Y \times Z \times C}\)  are collapsed to a single height plane to generate bird's-eye-view features \(\mathbf {B} \in \mathbb {R}^{X \times Y \times C}\) . BEV grids greatly reduce the computational overhead while offering similar detection performance to 3D voxel grids [1]}, motivating their use in our network. We concatenate the vertical axis \(z\)  of the voxel grid \(\mathbf {V}\)  along the channel dimension \(c\)  to form a BEV grid \(\mathbf {\tilde{B}} \in \mathbb {R}^{X \times Y \times Z*C}\) . The number of channels are reduced using a 1x1 convolution + BatchNorm + ReLU layer (see BEV Channel Reduce in Figure REF ), which retrieves the original number of channels \(C\)  while learning the relative importance of each height slice, resulting in a BEV grid \(\mathbf {B} \in \mathbb {R}^{X \times Y \times C}\) .
